- published: 03 Aug 2011
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In probability theory, an event is a set of outcomes of an experiment (a subset of the sample space) to which a probability is assigned. A single outcome may be an element of many different events, and different events in an experiment are usually not equally likely, since they may include very different groups of outcomes. An event defines a complementary event, namely the complementary set (the event not occurring), and together these define a Bernoulli trial: did the event occur or not?
Typically, when the sample space is finite, any subset of the sample space is an event (i.e. all elements of the power set of the sample space are defined as events). However, this approach does not work well in cases where the sample space is uncountably infinite, most notably when the outcome is a real number. So, when defining a probability space it is possible, and often necessary, to exclude certain subsets of the sample space from being events (see Events in probability spaces, below).
Probability theory is the branch of mathematics concerned with probability, the analysis of random phenomena. The central objects of probability theory are random variables, stochastic processes, and events: mathematical abstractions of non-deterministic events or measured quantities that may either be single occurrences or evolve over time in an apparently random fashion.
It is not possible to predict precisely results of random events. However, if a sequence of individual events, such as coin flipping or the roll of dice, is influenced by other factors, such as friction, it will exhibit certain patterns, which can be studied and predicted. Two representative mathematical results describing such patterns are the law of large numbers and the central limit theorem.
As a mathematical foundation for statistics, probability theory is essential to many human activities that involve quantitative analysis of large sets of data. Methods of probability theory also apply to descriptions of complex systems given only partial knowledge of their state, as in statistical mechanics. A great discovery of twentieth century physics was the probabilistic nature of physical phenomena at atomic scales, described in quantum mechanics.
Theory is a contemplative and rational type of abstract or generalizing thinking, or the results of such thinking. Depending on the context, the results might for example include generalized explanations of how nature works. The word has its roots in ancient Greek, but in modern use it has taken on several different related meanings. A theory is not the same as a hypothesis. A theory provides an explanatory framework for some observation, and from the assumptions of the explanation follows a number of possible hypotheses that can be tested in order to provide support for, or challenge, the theory.
A theory can be normative (or prescriptive), meaning a postulation about what ought to be. It provides "goals, norms, and standards". A theory can be a body of knowledge, which may or may not be associated with particular explanatory models. To theorize is to develop this body of knowledge.
As already in Aristotle's definitions, theory is very often contrasted to "practice" (from Greek praxis, πρᾶξις) a Greek term for "doing", which is opposed to theory because pure theory involves no doing apart from itself. A classical example of the distinction between "theoretical" and "practical" uses the discipline of medicine: medical theory involves trying to understand the causes and nature of health and sickness, while the practical side of medicine is trying to make people healthy. These two things are related but can be independent, because it is possible to research health and sickness without curing specific patients, and it is possible to cure a patient without knowing how the cure worked.
Khan Academy is a non-profit educational organization created in 2006 by educator Salman Khan with the aim of providing a free, world-class education for anyone, anywhere. The organization produces short lectures in the form of YouTube videos. In addition to micro lectures, the organization's website features practice exercises and tools for educators. All resources are available for free to anyone around the world. The main language of the website is English, but the content is also available in other languages.
The founder of the organization, Salman Khan, was born in New Orleans, Louisiana, United States to immigrant parents from Bangladesh and India. After earning three degrees from the Massachusetts Institute of Technology (a BS in mathematics, a BS in electrical engineering and computer science, and an MEng in electrical engineering and computer science), he pursued an MBA from Harvard Business School.
In late 2004, Khan began tutoring his cousin Nadia who needed help with math using Yahoo!'s Doodle notepad.When other relatives and friends sought similar help, he decided that it would be more practical to distribute the tutorials on YouTube. The videos' popularity and the testimonials of appreciative students prompted Khan to quit his job in finance as a hedge fund analyst at Connective Capital Management in 2009, and focus on the tutorials (then released under the moniker "Khan Academy") full-time.
Probability is the measure of the likelihood that an event will occur. Probability is quantified as a number between 0 and 1 (where 0 indicates impossibility and 1 indicates certainty). The higher the probability of an event, the more certain we are that the event will occur. A simple example is the tossing of a fair (unbiased) coin. Since the coin is unbiased, the two outcomes ("head" and "tail") are equally probable; the probability of "head" equals the probability of "tail." Since no other outcome is possible, the probability is 1/2 (or 50%) of either "head" or "tail". In other words, the probability of "head" is 1 out of 2 outcomes and the probability of "tail" is also, 1 out of 2 outcomes.
These concepts have been given an axiomatic mathematical formalization in probability theory (see probability axioms), which is used widely in such areas of study as mathematics, statistics, finance, gambling, science (in particular physics), artificial intelligence/machine learning, computer science, game theory, and philosophy to, for example, draw inferences about the expected frequency of events. Probability theory is also used to describe the underlying mechanics and regularities of complex systems.
We give you an introduction to probability through the example of flipping a quarter and rolling a die. Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/probability/independent-dependent-probability/basic_probability/e/dice_probability?utm_source=YT&utm;_medium=Desc&utm;_campaign=ProbabilityandStatistics Watch the next lesson: https://www.khanacademy.org/math/probability/independent-dependent-probability/basic_probability/v/probability-space-exercise-example?utm_source=YT&utm;_medium=Desc&utm;_campaign=ProbabilityandStatistics Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs ...
This short video introduces two important concepts in Probability, that of a sample space (outcome space) and that of an event.
MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013 View the complete course: http://ocw.mit.edu/18-S096F13 Instructor: Choongbum Lee This lecture is a review of the probability theory needed for the course, including random variables, probability distributions, and the Central Limit Theorem. *NOTE: Lecture 4 was not recorded. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Use this video to help find probabliity of simple events
In this video, I go over the definition of an event and a sample space, i discuss the importance of setting your sample space, I go over the three axioms of probability and use an example with dice as an application
Concepts. Probability formulas. Examples with decks of cards, marbles and Venn diagrams. Addition rule. For more free math videos, visit: http://www.professorserna.com
Event (probability theory) In probability theory, an event is a set of outcomes of an experiment (a subset of the sample space) to which a probability is assigned.A single outcome may be an element of many different events, and different events in an experiment are usually not equally likely, since they may include very different groups of outcomes. =======Image-Copyright-Info======= Image is in public domain Author-Info: User:Booyabazooka Image Source: https://en.wikipedia.org/wiki/File:Venn_A_subset_B.svg =======Image-Copyright-Info======== -Video is targeted to blind users Attribution: Article text available under CC-BY-SA image source in video https://www.youtube.com/watch?v=kSVd85NjpMM
Comparing theoretical and experimental probability.
Defining discrete and continuous random variables. Working through examples of both discrete and continuous random variables. Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/e/constructing-probability-distributions?utm_source=YT&utm;_medium=Desc&utm;_campaign=ProbabilityandStatistics Watch the next lesson: https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/discrete-probability-distribution?utm_source=YT&utm;_medium=Desc&utm;_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/random-variables?utm_source=YT&utm;_medium=Desc&utm;_campaig...
Basics of Probability Theory/ Kolmogorov Axioms
We give you an introduction to probability through the example of flipping a quarter and rolling a die. Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/probability/independent-dependent-probability/basic_probability/e/dice_probability?utm_source=YT&utm;_medium=Desc&utm;_campaign=ProbabilityandStatistics Watch the next lesson: https://www.khanacademy.org/math/probability/independent-dependent-probability/basic_probability/v/probability-space-exercise-example?utm_source=YT&utm;_medium=Desc&utm;_campaign=ProbabilityandStatistics Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs ...
This short video introduces two important concepts in Probability, that of a sample space (outcome space) and that of an event.
MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013 View the complete course: http://ocw.mit.edu/18-S096F13 Instructor: Choongbum Lee This lecture is a review of the probability theory needed for the course, including random variables, probability distributions, and the Central Limit Theorem. *NOTE: Lecture 4 was not recorded. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Use this video to help find probabliity of simple events
In this video, I go over the definition of an event and a sample space, i discuss the importance of setting your sample space, I go over the three axioms of probability and use an example with dice as an application
Concepts. Probability formulas. Examples with decks of cards, marbles and Venn diagrams. Addition rule. For more free math videos, visit: http://www.professorserna.com
Event (probability theory) In probability theory, an event is a set of outcomes of an experiment (a subset of the sample space) to which a probability is assigned.A single outcome may be an element of many different events, and different events in an experiment are usually not equally likely, since they may include very different groups of outcomes. =======Image-Copyright-Info======= Image is in public domain Author-Info: User:Booyabazooka Image Source: https://en.wikipedia.org/wiki/File:Venn_A_subset_B.svg =======Image-Copyright-Info======== -Video is targeted to blind users Attribution: Article text available under CC-BY-SA image source in video https://www.youtube.com/watch?v=kSVd85NjpMM
Comparing theoretical and experimental probability.
Defining discrete and continuous random variables. Working through examples of both discrete and continuous random variables. Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/e/constructing-probability-distributions?utm_source=YT&utm;_medium=Desc&utm;_campaign=ProbabilityandStatistics Watch the next lesson: https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/discrete-probability-distribution?utm_source=YT&utm;_medium=Desc&utm;_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/random-variables?utm_source=YT&utm;_medium=Desc&utm;_campaig...
Basics of Probability Theory/ Kolmogorov Axioms
MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013 View the complete course: http://ocw.mit.edu/18-S096F13 Instructor: Choongbum Lee This lecture is a review of the probability theory needed for the course, including random variables, probability distributions, and the Central Limit Theorem. *NOTE: Lecture 4 was not recorded. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Explores basic quantities related to events: sample space, event probability, Bayes' Theorem, Total Probability Theorem, Mutually Exclusive events, Independent Events, Bernoulli Trials, etc.
Probability is the examination of uncertain processes, but it's useful for far more than games of chance: http://www.gresham.ac.uk/lectures-and-events/probability-and-its-limits The modern theory of probability is considered to have begun in 1654 with an exchange of letters between Blaise Pascal and Pierre de Fermat, and has developed since then into the discipline which examines uncertain processes. For example, although on tossing a coin you have no idea whether you will obtain heads or tails we know that if you keep doing it then in the long run it is very likely that the proportion of heads will be close to a half. The lecture will discuss this and other examples of random processes e.g. random walks and Brownian motion. The transcript and downloadable versions of the lecture are ava...
Basic refresher on probability theory for machine learning, by Maximilian Sölch. We suggest you play at 125% speed. See http://brml.org.
Probability Theory and Applications by Prof. Prabha Sharma,Department of Mathematics,IIT Kanpur.For more details on NPTEL visit http://nptel.ac.in.
Review of Probability Theory link: http://cs229.stanford.edu/section/cs229-prob.pdf
Lesson 2 - Sample Space, Events and Compound Events
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